Are You Smiling as a Celebrity? Latent Smile and Gender Recognition

Abstract

Person gender detection is an important feature in many vision-based research fields including surveillance, human computer interaction, Biometrics, stratified behavior understanding, and content-based indexing. Researchers are still facing big challenges to establish automated systems to recognize gender from images where human face represents the most important source of information. In the present study, we elaborated and validated a methodology for gender perception by transfer learning. First, the face is located and the corresponding cropped image is fed to a pre-trained convolutional neural network, the generated deep “latent” features are used to train a linear-SVM classifier. The overall classification performance reached $90.69\%$ on the FotW validation set and $91.52\%$ on the private test set.

In this paper, we investigated also whether these features can deliver a smile recognizer. A similar trained architecture for classification of smiling and non-smiling faces gave a rate of $88.14\%$ on the validation set and $82.12\%$ on the private test set.